EdgeConnect: Structure Guided Image Inpainting using Edge Prediction

By Kamyar Nazeri, Eric Ng, Tony Joseph, Faisal Z. Qureshi, Mehran Ebrahimi. 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), 2019

532 citations recorded by the source corpus.

In recent years, many deep learning techniques have been applied to the image inpainting problem: the task of filling incomplete regions of an image. However, these models struggle to recover and/or preserve image structure especially when significant portions of the image are missing. We propose a two-stage model that separates the inpainting problem into structure prediction and image completion. Similar to sketch art, our model first predicts the image structure of the missing region in the form of edge maps. Predicted edge maps are passed to the second stage to guide the inpainting process. We evaluate our model end-to-end over publicly available datasets CelebA, CelebHQ, Places2, and Paris StreetView on images up to a resolution of 512 × 512. We demonstrate that this approach outperforms current state-of-the-art techniques quantitatively and qualitatively.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/1532750abbb9eac71592ff8f81f1a1a3f7ed9a54

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Citations in the corpus

Palette: Image-to-Image Diffusion Models (2021, background): “features [Iizuka et al. 2017; Kim et al. 2021; Liu et al. 2020; Nazeri et al. 2019; Yi et al. 2020; Yu et al. 2018b, 2019], and they lack diversity in their outputs [Zhao et al.”
Recurrent Feature Reasoning for Image Inpainting (2020, methodology): “RFR-NetPRVSGatedConv PConvEdgeConnectMasked Image Figure 5.”
RePaint: Inpainting using Denoising Diffusion Probabilistic Models (2022, methodology): “As image inpainting requires a high-level semantic context, and to explicitly include it in the generation pipeline, there exist hand-crafted architectural designs such as Dilated Convolutions [16, 44] to increase the receptive field, Partial Convolutions [19] and Gated Convolutions [47] to guide th”
M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection (2021, methodology): “Perceptual Loss Perceptual loss is usually used in face inpainting approaches [41, 69] to measure the similarity between the restored faces and corresponding complete faces.”